Damen, D and Hogg, DC (2012) Explaining Activities as Consistent Groups of Events: A Bayesian Framework Using Attribute Multiset Grammars. International Journal of Computer Vision, 98 (1). 83 - 102 (20). ISSN 0920-5691
Abstract
We propose a method for disambiguating uncertain detections of events by seeking global explanations for activities. Given a noisy visual input, and exploiting our knowledge of the activity and its constraints, one can provide a consistent set of events explaining all the detections. The paper presents a complete framework that starts with a general way to formalise the set of global explanations for a given activity using attribute multiset grammars (AMG). An AMG combines the event hierarchy with the necessary features for recognition and algebraic constraints defining allowable combinations of events and features. Parsing a set of detections by such a grammar finds a consistent set of events that satisfies the activity’s constraints. Each parse tree has a posterior probability in a Bayesian sense. To find the best parse tree, the grammar and a finite set of detections are mapped into a Bayesian network. The set of possible labellings of the Bayesian network corresponds to the set of all parse trees for a given set of detections.We compare greedy, multiple-hypotheses trees, reversible jump MCMC, and integer programming for finding the Maximum a Posteriori (MAP) solution over the space of explanations. The framework is tested for two applications; the activity in a bicycle rack and around a building entrance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2012, Springer Verlag. This is an author produced version of a paper published in International Journal of Computer Vision. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at link.springer.com. |
Keywords: | Activity analysis, Event recognition, Global explanations |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 May 2013 08:13 |
Last Modified: | 23 Jan 2018 14:22 |
Published Version: | http://dx.doi.org/10.1007/s11263-011-0497-0 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s11263-011-0497-0 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75560 |